M215: Applied Statistical Analysis Mathematica Course

Course Objective This course is designed to give experience with the statistical tools that are available in Mathematica. Using real-world and simulated datasets, participants will import and analyze data, work with statistical distributions, and visualize results.

Presenter The course is presented by a Wolfram Research senior developer or a Wolfram Education Group certified instructor.

Target Audience The course is designed for people who work with data and distributions and wish to improve their skills at using Mathematica for performing statistical analyses of data. Typical attendees include engineers, physicists, analysts in finance, and those in the physical sciences and the life and medical sciences.

Delivery Type Courses are delivered as instructor-led classes in computer classroom facilities or as online classes over the web. Course topics are presented with alternating sessions of lectures and exercises. All classes feature low student-teacher ratios.

Syllabus This course is organized into six segments.

Descriptive Statistics
Descriptive statistics and visualization of univariate and multivariate data; working with continuous and categorical data; clustering and smoothing of data
Statistical Distributions
Working with theoretical and empirical statistical distributions; descriptive statistics and functions of random variables; visualizations of distributions; random number generation; transformations; empirical distributions and bootstrapping
Hypothesis Testing
Common parametric tests such as t and Chi² tests; nonparametric tests of location and scale; distributional goodness of fit tests; confidence intervals and power curves
Model Fitting
Linear, nonlinear, and generalized linear model fitting; logistic models with nominal predictors; Poisson count models; ANOVA models
Model Refinements and Diagnostics
Obtaining and visualizing diagnostics for models; comparing models; model transformations; weighted regression
Special Topics
Maximum likelihood estimation; implicitly defined models; robust regression using iterative re-weighting; alternate metrics or merit functions for fitting data to curves

Course Materials Each attendee will be provided with Mathematica course notebooks and access to the current version of Mathematica. The course notebooks require Mathematica or Wolfram CDF Player. For attendees participating in classroom-based sessions, course materials are distributed in print and on CD-ROM, and are yours to keep; a computer running Mathematica is available for your use during class. For attendees participating in online classes, a download of the course materials is provided; a temporary Mathematica training license is provided upon request.

Prerequisites Course attendees are expected to have basic familiarity with Mathematica approximately equivalent to that provided by "M101: A First Course in Mathematica." Attendees should also have basic knowledge of descriptive statistics, mathematical statistics, hypothesis testing, ANOVA, and regression.